Viewing 1-10 of 369 papers
  • Analyzing Compositionality in Visual Question Answering

    Sanjay Subramanian, Sameer Singh, Matt GardnerNeurIPS • ViGIL Workshop2020Since the release of the original Visual Question Answering (VQA) dataset, several newer datasets for visual reasoning have been introduced, often with the express intent of requiring systems to perform compositional reasoning. Recently, transformer models pretrained on large amounts of images and… more
  • Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image

    Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, Yejin Choi ECCV2020Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man… more
  • Break It Down: A Question Understanding Benchmark

    Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan BerantTACL2020Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through… more
  • Adversarial Filters of Dataset Biases

    Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi ICML2020Large neural models have demonstrated humanlevel performance on language and vision benchmarks such as ImageNet and Stanford Natural Language Inference (SNLI). Yet, their performance degrades considerably when tested on adversarial or out-of-distribution samples. This raises the question of whether… more
  • Transformers as Soft Reasoners over Language

    Peter Clark, Oyvind Tafjord, Kyle RichardsonIJCAI2020AI has long pursued the goal of having systems reason over explicitly provided knowledge, but building suitable representations has proved challenging. Here we explore whether transformers can similarly learn to reason (or emulate reasoning), but using rules expressed in language, thus bypassing a… more
  • Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

    Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, Peter ClarkIJCAI2020Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the… more
  • TransOMCS: From Linguistic Graphs to Commonsense Knowledge

    Hongming Zhang, Daniel Khashabi, Yangqiu Song, Dan RothIJCAI2020Commonsense knowledge acquisition is a key problem for artificial intelligence. Conventional methods of acquiring commonsense knowledge generally require laborious and costly human annotations, which are not feasible on a large scale. In this paper, we explore a practical way of mining commonsense… more
  • Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses

    Erfan Sadeqi Azer, Daniel Khashabi, Ashish Sabharwal, Dan RothACL2020Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues. While alternative proposals have been well-debated and adopted in other fields, they remain rarely… more
  • A Formal Hierarchy of RNN Architectures

    William. Merrill, Gail Garfinkel Weiss, Yoav Goldberg, Roy Schwartz, Noah A. Smith, Eran YahavACL2020We develop a formal hierarchy of the expressive capacity of RNN architectures. The hierarchy is based on two formal properties: space complexity, which measures the RNN's memory, and rational recurrence, defined as whether the recurrent update can be described by a weighted finite-state machine. We… more
  • A Mixture of h-1 Heads is Better than h Heads

    Hao Peng, Roy Schwartz, Dianqi Li, Noah A. SmithACL2020Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks. Evidence has shown that they are overparameterized; attention heads can be pruned without significant performance loss. In this work, we instead "reallocate" them… more